Workshops

SPARS2017 5th-8th June 2017

In collaboration with the MacSeNet project, the network bid and successfully won the right to host SPARS 2017 at IST in Lisbon, Portugal. This is a large workshop, 150-200 attendees, which is well established within a relevant field. On the first day of this event there was a showcase of the two MSCA ITN projects. This was achieved in two ways, first there was an allocated slot in the oral programme for the Network Co-ordinator, Prof. Mark Plumbley, to explain the ethos of the MCSA programme and the 2 projects that have been funded. Secondly, there was a special poster session for the Fellows to present the research work they had been doing during the project.

This one-day workshop, organised in collaboration with the MacSeNet Innovative Training Network, will include invited keynote talks by Karin Schnass (Universität Innsbruck, Austria) and Jean-Luc Starck (CEA-Saclay, France), oral presentations and posters. The talks and posters will include theoretical advances in sparse representations, dictionary learning and compressed sensing, as well as advances in areas such as brain imaging and MRI, hyperspectral imaging, audio and visual signal processing, inverse imaging problems, and graph-structured signals.

Scientific Training Events

First Summer Spring School 2016 held in Fraunhofer IDMT, Ilmenau, Germany 4th-8th April 2016

The SpaRTaN-MacSeNet Spring School on Sparse Representations and Compressed Sensing Spring School was aimed at graduate students, researchers and industry professionals working in this fast moving and exciting area. The five day school was split into two components, during three days, a panel of experts offered lectures and tutorials covering the theory of sparse representations, compressed sensing and related topics; alongside applications of these methods in areas such as image processing, audio signal processing, and signal processing on graphs. The remaining two days were devoted to software carpentry, giving researchers the computing skills they need to get more done in less time and with less pain

Second Summer School 2017 held in Lisbon, Portugal, 31st May-2nd June 2017

We held our Second Summer School for scientific training in Lisbon, Portugal. We co-located the event with the SPARS Workshop as this enabled us to secure great speakers and get a wide audience from outside the network. The courses consisted of the following speakers and topics:

In addition to these speakers there was a poster session for the Fellows and external participants to practise presenting their posters prior to SPARS and a panel discussion where several of our tutors answered questions from the tutees about the current field and where they think it will head in the future. The panel was chaired by Prof. Mário Figueiredo and questions were taken both on the day and in advance via a post-it-note board.

Research Skills Training Events (Network Only)

First Training Week

The network has held 2 initial training weeks covering transferable skills for researchers and all our ESRs have attended one of these weeks. The first was in Surrey in September 2015 and the second preceded the Ilmenau Spring School in April 2016. Both training weeks focussed on transferable skills aligned to the Vitae Researcher Development Framework and were provided by SURREY’s Researcher Development Team who as well as providing courses for SURREY’s researchers have had experience running similar events for other ITNs and SEPNet (the South East Physics Network).

We covered topics which were important to all researchers as well as some of the more specific areas that are likely to come up for our highly mobile ESRs. The theme of the first sessions of both training weeks reflected this by covering unwritten rules, those between different cultures caused by different countries and languages as well as those caused by different stages of career or different education experiences. The other sessions covered the skills researchers need to plan and develop their career, they were formally introduced to the Vitae Researcher Development Framework and were encouraged to look at the skills a researcher needs. In week one this day included an employer panel discussion which allowed the ESRs to talk to senior researchers or managers in SMEs, International Companies, Start-ups and Academia. The panellists were asked to come with an idea of what they looked for when recruiting researchers so that the ESRs could ask questions about what it was like to work in various environments and what skills were specific or common across employers. Following this the ESRs were able to use the Action Planning tools to help them identify areas where they needed further training. During the second training week the employer panel was swapped for networking skills to enable them to make the most of the industry and academic visitors at the summer school that was about to follow. Other sessions looked at the skills researchers need to present their research and engage with different audiences and Dr Sophie Wehrens, the MacSeNet Ethics Advisor, prepared a session on understanding ethics in research with examples for the ESRs to discuss and analyse to give them an insight into how ethics applies to their research.

Second Training Week

From the 16th - 23rd November 2016 we ran the Second Training Week at EPFL in Lausanne, Switzerland. The first three days focussed on Science 2.0 topics including science communication, open access, publishing and data management. It was followed up by a three-day sandpit where the Fellows worked on small collaborative projects and put into practice the tools and skills they had learnt during the previous training weeks.

Third Training Week

The first week in April 2017 saw our Third Training Week, hosted in Edinburgh, UK. Timed to coincide with the Edinburgh International Science Festival the aim of the week was communication and entrepreneurship. Sessions covered innovation and entrepreneurship, graphic design and data visualisation, communicating to the press, intellectual property, licencing, financing and pitching. The week was ended by a morning with a voice coach and a public engagement session on the streets of Edinburgh.

Project Administrator

Contact

The SpaRTaN Initial Training Network will train a new generation of interdisciplinary researchers in sparse representations and compressed sensing, contributing to Europe’s leading role in scientific innovation.

By bringing together leading academic and industry groups with expertise in sparse representations, compressed sensing, machine learning and optimisation, and with an interest in applications such as hyperspectral imaging, audio signal processing and video analytics, this project will create an interdisciplinary, trans-national and inter-sectorial training network to enhance mobility and training of researchers in this area.

SpaRTaN is funded under the FP7-PEOPLE-2013-ITN call and is part of the Marie Curie Actions — Initial Training Networks (ITN) funding scheme: Project number - 607290

Check out the videos our Fellows have made about their research:

Recruitment is now closed

SpaRTaN is an EU-funded Marie Curie Initial Training Network, bringing together leading academic and industry groups to train a new generation of interdisciplinary researchers in sparse representations and compressed sensing, with applications in areas such as hyperspectral imaging, audio signal processing and video analytics.

There are eight Marie Curie Early Stage Researcher (ESR) positions, which allow the researcher to work towards a PhD, and two Marie Curie Experienced Researcher (ER) Positions for candidates who already have a PhD or equivalent research experience. The ESRs will be recruited to start during the first half of 2015 for a duration of 36 months and the ERs will be recruited to start towards the end of 2015 for a duration of 24 months.

Each ESR and ER will have secondments linked to their research to other partners in the network. They will also attend ITN progress meetings and Training events throughout Europe and possibly conferences and events internationally.

Marie Curie ESRs and ERs are paid a competitive salary which is adjusted for their host country. Please see the individual positions below to find the annual salary for that host country (figures are given in Euros prior to employer and employee tax being deducted). Since the ESR and ER positions include secondments to other hosts and for the researcher to move countries the EU also provides a Mobility Allowance, this is higher for researchers who have a family (family is defined as persons linked to the researcher by (i) marriage, or (ii) a relationship with equivalent status to a marriage recognised by the national legislation of the country of the beneficiary or of the nationality of the researcher, or (iii) dependent children who are actually being maintained by the researcher).

ESRs should be within four years of the diploma granting them access to doctorate studies at the time of recruitment. ERs need to be within the first five years of years of their research career, i.e from the date of their diploma granting access to doctorate studies. (See the figure to the right)

In addition, to be eligible for a position as a Marie Curie Early Stage Researcher or Experienced Researchers you must not have spent more than 12 months in the host country in the 3 years prior to starting.

In order to be sure that you meet these requirements please fill in the eligibility form and include it with your application.

Human listeners appear to have an innate and effortless ability to isolate and attend to one sound source while suppressing others. This remarkable phenomenon is called the cocktail party effect. We would like to be able to perform the same task for musical audio signals, and extract a signal corresponding to one instrument from a stereo (or mono, or 5.1 surround sound) mixture of musical audio. Since the mixing and source processes are not known beforehand, this problem is often known as blind source separation (BSS).

The candidate will be responsible for developing source separation algorithms, particularly for the difficult case of more sources than sensor channels. We wish to achieve excellent perceptual quality of reconstructed sources for applications such as: music separation and remixing; audio denoising, restoration and enhancement; music information retrieval, and interactive music performance systems. Methods to be studied include sparse representations, time-frequency masking, Bayesian probabilistic inference, non-negative matrix factorization (NMF), beamforming, and independent component analysis (ICA). This project will also investigate the use of knowledge of the notes from a musical score to identify notes from different instruments to help the separation process.

CVSSP is one of the major research centres of Surrey’s Department of Electronic Engineering (EE), the top ranked UK EE department in both the RAE 2008 and in the national league tables. CVSSP is the largest research centres in the UK focusing on Computer Vision, graphics and signal processing, with 120+ members comprising academic and support staff, research fellows and PhD students.

Requirements

The successful applicant is expected to have an excellent mathematical and programming background, with an MSc or equivalent related to signal processing and audio engineering. Programming skills in Matlab or C/C++ are required.

Applications

Please ensure your application includes your completed eligibility form and a letter of motivation including a maximum 1-page statement explaining how your research interests, skills and experience are relevant to the position;

Human perception of sounds is much more advanced than any technical system so far created. However, transcription of polyphonic music (i.e. converting music with many instruments or notes at the same time into a written "score" notation) is a problem that is hard even for trained human listeners. Previous research into automatic music transcription (AMT) using sparse representations. However, it was discovered that the representations need to be clustered into groups, and that there are strong correlations over time. Recent developments in structured sparse representations, such as group sparsity and tree-structured sparsity, offer the potential to significantly improve these results, as a fruitful tool to encode prior knowledge about the physics of signal processing and machine learning problems.

In this PhD project, the candidate will investigate new methods for automatic music transcription, focussing on dictionary learning methods related for sparse and structured sparse representations. Methods to be investigated include the use group sparsity as part of the decomposition learning process; tree-structured sparsity, with notes modelled as parts of chords with different likelihoods; and structuring over time instead of independent decomposition for each time frame.

CVSSP is one of the major research centres of Surrey’s Department of Electronic Engineering (EE), the top ranked UK EE department in both the RAE 2008 and in the national league tables. CVSSP is the largest research centres in the UK focusing on Computer Vision, graphics and signal processing, with 120+ members comprising academic and support staff, research fellows and PhD students.

Requirements

The successful applicant is expected to have an excellent mathematical and programming background, with an MSc or equivalent related to signal processing and audio engineering. Programming skills in Matlab or C/C++ are required.

Applications

Please ensure your application includes your completed eligibility form and a letter of motivation including a maximum 1-page statement explaining how your research interests, skills and experience are relevant to the position;

One PhD position is available jointly at the Institute for Digital Communications and the Brain Research Imaging Centre at the University of Edinburgh, UK. The selected candidate will study the next generation of compressed sensing (CS) techniques for accelerated acquisition in MRI. The project will explore the benefits of CS advanced imaging modalities, such as quantitative imaging, MR spectroscopy and diffusion tensor imaging.

This PhD will investigate the next generation of compressed sensing (CS) techniques for accelerated acquisition in MRI.

Magnetic Resonance Imaging has already shown itself to be an ideal candidate for the application of compressed sensing theory. Excellent image reconstruction has been shown to be possible from undersampled k-space measurements through the application of compressed sensing principles and algorithms. However the real challenge and benefits lie in tackling advanced MR imaging techniques, such as quantitative MRI, MR spectroscopy and diffusion tensor imaging. These techniques require very long acquisition times and can suffer from bad motion artefacts induced during acquisition. These problems go beyond traditional CS solutions, and to tackle them will require the development of new structural signal models and sampling strategies.

The Early Stage Researcher on this project will benefit from the partnership between IDCOM and BRIC in the University of Edinburgh and the other academic institutions and industrial partners within the SpaRTaN project. They will attend initial training events and be exposed to the research activities of all participants at regular six monthly progress meetings. They will engage in training events and secondments to other project partners.

Requirements

Highly motivated, excellent candidates should ideally hold a valid Masters degree with a specialisation in medical imaging and/or signal processing and should be eligible for immediate admission on the PhD programme at the University of Edinburgh.

Applications

Please ensure your application includes your completed eligibility form and a letter of motivation including a maximum 1-page statement explaining how your research interests, skills and experience are relevant to the position;

Audio-visual archives are so huge that tagging operations must be particularly computationally efficient and scalable. Machine learning techniques based on semi-supervised learning have proved particularly efficient at reducing expert interactions with large volumes of data, and therefore offer an important technological platform for this problem. In this project we will investigate deep learning algorithms operating on patch based dictionaries of features trained on specific tagging tasks; tackle hierarchies of problems, from classifying musicians to classifying audio-visual events; and contribute new technologies to organise, interact with it and offer new human-computer interfaces to explore the Montreux Jazz Festival audio-visual archives.

This is one of two doctoral student positions open at the Laboratory of Signal Processing 2, LTS2, at EPFL, Lausanne, Switzerland, focusing on the application of compressive sensing and sparsity based methods to machine learning with applications to audio-visual data.

The LTS2 is specialized in signal/image/data processing, graph and networks analysis as well as machine learning.

The lab offers a stimulating research environment with an open minded and collaborative team, state of the art IT facilities (multicore servers and GPU units), as well as acoustics facilities (anechoic and reverberant rooms).

Conditions of employment

This is a full time position as PhD researcher within the Laboratory of Signal Processing 2. The candidate will dedicate its time to research as well as be interacting with Master students and participate in teaching.

Requirements

A candidate with an MSc degree or equivalent either

in electrical engineering, computer science or applied mathematics with a strong interest in acoustics.

in acoustics with an interest for mathematics and a good experience in programming and scientific numerical computations.

Experience with programming in one or more languages is mandatory, a good knowledge of Matlab or Python is a plus. We expect a good level of English. Ability to travel within the network is essential, as currently one position includes secondments in Edinburgh (three months) and Finland (three months) and the other in London (six months).

Applications

To apply the candidate must register and get admitted to the EPFL doctoral school « EDEE » before 15th of January 2015 or if the post is not filled after this date « EDIC » before 15th of April 2015.

Please ensure your application includes your completed eligibility form and a letter of motivation including a maximum 1-page statement explaining how your research interests, skills and experience are relevant to the position;

One of the main promised impacts of CS is the development of new, more efficient sensors. Recently, it has been shown that CS measurements can be radically quantised to only a single bit (0/1), and yet the original signal can still be efficiently and robustly recovered. This striking result could open the way to dramatically simplified sensors, such as cameras with ultra-simple analog and digital electronics. In parallel, many current computer vision and augmented reality applications describe image content by means of localised binary descriptors that drive classifiers. These binary descriptors can be implemented on mobile platforms very efficiently. There is therefore a convergence of technologies based on 1-bit measurements that has not yet been explored. The outcome of this research could result in brand new imaging chips for mobile applications that would be ultra efficient in terms of power consumption but would also allow the direct extraction of low level features for computer vision applications. In this project, we will interact with a hardware group and help design novel hardware based on 1-bit sensing; demonstrate a standalone visual sensor that directly collects 1-bit measurements; decode measurements to form images; and apply with no decoding for high-level computer vision and augmented reality applications.

This is one of two doctoral student positions open at the Laboratory of Signal Processing 2, LTS2, at EPFL, Lausanne, Switzerland, focusing on the application of compressive sensing and sparsity based methods to machine learning with applications to audio-visual data.

The LTS2 is specialized in signal/image/data processing, graph and networks analysis as well as machine learning.

The lab offers a stimulating research environment with an open minded and collaborative team, state of the art IT facilities (multicore servers and GPU units), as well as acoustics facilities (anechoic and reverberant rooms).

Conditions of employment

This is a full time position as PhD researcher within the Laboratory of Signal Processing 2. The candidate will dedicate its time to research as well as be interacting with Master students and participate in teaching.

Requirements

A candidate with an MSc degree or equivalent either

in electrical engineering, computer science or applied mathematics with a strong interest in acoustics.

in acoustics with an interest for mathematics and a good experience in programming and scientific numerical computations.

Experience with programming in one or more languages is mandatory, a good knowledge of Matlab or Python is a plus. We expect a good level of English. Ability to travel within the network is essential, as currently one position includes secondments in Edinburgh (three months) and Finland (three months) and the other in London (six months).

Applications

To apply the candidate must register and get admitted to the EPFL doctoral school « EDEE » before 15th of January 2015 or if the post is not filled after this date « EDIC » before 15th of April 2015.

Please ensure your application includes your completed eligibility form and a letter of motivation including a maximum 1-page statement explaining how your research interests, skills and experience are relevant to the position;

Closing Date for Applications: 2015-2-15 or 2015-04-15 (if not filled)

The dominant paradigm in sparsity-based regularization of inverse problems is that of synthesis sparsity, where the unknown signal is modeled as having a sparse representation on a dictionary of functions. Originally these dictionaries were fixed (e.g. wavelets bases or frames) but the current state-of-the-art methods use dictionaries that are learned from data. Dictionary learning methods were initially limited to simple Gaussian denoising problems, and have only recently been extended to harder problems, such as image deconvolution and reconstruction, or to problems involving non-Gaussian noise.

Analysis formulations are dual of the synthesis ones, in that the unknown signal is modeled as yielding a sparse result, when a so-called analysis operator is applied to it. Although analysis formulations have been widely used in denoising, deconvolution, and reconstruction, with Gaussian and other types of observation noise, only fixed analysis operators have typically been used. Very recently, the problem of learning operators for analysis sparsity formulations has started to be addressed, but so far only for the classical Gaussian denoising problems. The objective of this work is to extend analysis dictionary learning to scenarios harder than Gaussian denoising, namely, deconvolution, reconstruction, and non-Gaussian observations.

Requirements

Candidate are expected to hold an MSc degree (or equivalent), in electrical engineering, computer science, applied mathematics, or related areas, with a solid background on mathematics and, preferably, signal processing. Candidates are also expected to have a good scientific programming experience, preferably in Matlab and/or Python, a very good level of English, and availability to travel within the network.

Applications

To apply for the position, please provide:

(i) a letter of motivation including a maximum 1-page statement explaining how your research interests, skills and experience are relevant to the position;

(ii) a CV including publication list;

(iii) names and contact details of three referees willing to write confidential letters of recommendation.

Closing Date for Applications: 2015-2-15 or 2015-04-15 (if not filled)

In Hyperspectral imaging (HSI), the sensors measure the electromagnetic energy scattered in their instantaneous field view in hundreds (even thousands) of spectral channels. The very high spectral resolution of HSI enables a precise identification of the sensed materials via spectroscopic analysis, facilitating countless applications; e.g., earth observation and remote sensing, food safety, pharmaceutical process monitoring and quality control, as well as biomedical, industrial, and forensic applications.

This projects focus on compressive hyperspectral imaging (CHSI), whereby data compression is implemented simultaneously with the acquisition, by computing a number of projections, termed measurements, of the original data onto a set of predesigned vectors. Assuming that the original data admits a sparse representation on a given basis or frame, it can be recovered from the projections by solving a suitable optimization problem. CHI is of paramount importance in spaceborne systems, due to the extremely large volumes of data collected by the imaging spectrometers onboard satellites, and the low bandwidth of the connections between them and the ground stations.

The success of CHSI stems from the very high spectral and spatial correlation of this type of data, meaning that it is compressible, i.e., it admits a representation on a given frame in which most of the coefficients are small and, thus, it is well approximated by sparse representations. Due to the huge number of optimization variables involved in a typical CHI problem (values between 10^8 and 10^10 are usual), the solutions of these optimization problems are very demanding from the computational point of view. To make the problem even more challenging, the sparse representations are not known beforehand and have to be learned from the measurements.

Objectives

The main goals of this project are a) to develop effective adaptive sparse representations for hyperspectral data, namely by exploiting that fact that the spectral vectors usually leave in a data-dependent low dimensional subspace, b) to develop optimal measurement matrices in the sense of minimal number of measurements, and c) to develop optimization strategies to infer simultaneously the sparse representation and the original hyperspectral image.

Requirements

Candidates should hold an MSc degree in Electrical and/or Computer Engineering, Computer Science, or Applied Mathematics. The required skills include, with a flexible balance, advanced programming skills (C/C++, Matlab),strong background in statistics, signal processing and/or optimization, a good knowledge of English, and a strong motivation to work as part of a team.

Applications

To apply for the position, please provide:

(i) a letter of motivation including a maximum 1-page statement explaining how your research interests, skills and experience are relevant to the position;

(ii) a CV including publication list;

(iii) names and contact details of three referees willing to write confidential letters of recommendation.

Machine learning methods are becoming widespread for signal processing, in most areas of science, engineering and the economy. These data-driven approaches require an explicit management of uncertainty. The PhD student is expected to develop methodologies to compute a degree of confidence in predictions, that allows subsequent users to appropriately take decisions. Within this project, we will particularly focus on high-dimensional problems with potentially a large number of observations and predictions based on convex optimization.

The 36 month project will be undertaken within the Computer Science Department of Ecole Normale Superieure, located in downtown Paris, within the INRIA/CNRS/ENS project-team SIERRA.

Medical imaging systems often face a trade-off between attainable image quality and admissible exposure to harmful radiation, thus entailing both low signal-to-noise ratio and missing measurements. Digital image filtering is becoming increasingly important towards dose reduction and as an effective means for regularization when dealing with missing measurements.

This project considers the analysis and modeling of image capture and reconstruction pipelines in direct as well as indirect imaging systems and the design of filtering and CT and tomosynthesis.

One Experienced Researcher position is available. The selected candidate will investigate and model the image formation and reconstruction pipelines for medical imaging and develop computationally efficient algorithms for image filtering and the necessary plug-in pipeline modules. Special emphasis is placed on non-Gaussian degradations (including signal-dependent and spatially-correlated noise) and on the use of sparsity for denoising/enhancement and for removing artifacts generated by highly attenuating features such as metal implants in limited angle acquisition systems.

Experience in medical imaging is a plus. Candidates are also expected to have good skills in scientific programming (preferably Matlab and C), proficiency in English, and availability to travel within the SpaRTaN network.

Applications

To apply for the position, please provide:

i. A letter of motivation (maximum one page) explaining how your research interests, skills and experience are relevant to the position.

ii. A detailed CV, including list of publications. If any, copies of the publications most relevant to the position.

iii. Names and contact details of three referees willing to write confidential letters of recommendation.

iv. Your completed eligibility form.

All materials should be emailed as a single PDF file to ni@noiselessimaging.com with 'Application SPARTAN ER2' in the subject line.

One Experienced Researcher position is available. The selected candidate will study and design computationally efficient algorithms for image, video, and multidimensional data restoration, including denoising, deblurring, blind deblurring and super-resolution. In particular, the considered algorithms will be based on a sparse representation with respect to data-driven adaptive transforms, where the adaptivity follows from a nonlocal spatial or spatio-temporal analysis of the data. The results shall be applicable to challenging problems of industrial significance.

Planned Secondments

Scientific: Instituto de Telecomunicações, Portugal, 3 months.

Requirements

Candidates should hold a master or doctoral degree in image processing, computer science and/or engineering, applied mathematics, or related areas, with a strong background in linear algebra. Experience in image-processing algorithms is essential. Candidates are also expected to have good skills in scientific programming (preferably Matlab and/or C), proficiency in English, and availability to travel within the SpaRTaN network.

Applications

To apply for the position, please provide:

i. A letter of motivation (maximum one page) explaining how your research interests, skills and experience are relevant to the position.

ii. A detailed CV, including list of publications. If any, copies of the publications most relevant to the position.

iii. Names and contact details of three referees willing to write confidential letters of recommendation.

iv. Your completed eligibility form.

All materials should be emailed as a single PDF file to ni@noiselessimaging.com with 'Application SPARTAN ER2' in the subject line.

Image super-resolution aims at constructing high resolution analog of a given low resolution image. Following a big success of CNNs in image classification, recently CNNs have been successfully applied to the problem of image super-resolution, achieving state-of-the-art performance. They take a full advantage of parallelization of computations and efficient implementation on GPUs.

This project considers the specific problem of image super-resolution, when the input are noisy raw data. Special emphasis in this project is on GPU realization.

One Experienced Researcher position is available. The selected candidate will develop an efficient CNN-based image super-resolution method from sensor raw data and implement the developed method on GPU.

Planned Secondments

Requirements

Candidates should hold a master or doctoral degree in image processing, computer science and/or engineering, applied mathematics, or related areas. Experience in image-processing, CNN and deep learning methods, or GPU programming are recommended. Candidates are also expected to have good skills in scientific programming (preferably Matlab and C), proficiency in English, and availability to travel within the SpaRTaN network.

Applications

To apply for the position, please provide:

i. A letter of motivation (maximum one page) explaining how your research interests, skills and experience are relevant to the position.

ii. A detailed CV, including list of publications. If any, copies of the publications most relevant to the position.

iii. Names and contact details of three referees willing to write confidential letters of recommendation.

iv. Your completed eligibility form.

All materials should be emailed as a single PDF file to ni@noiselessimaging.com with 'Application SPARTAN ER2' in the subject line.

* Salary adjusted for host country prior to employer and employee tax being deducted, as such the gross and net amount received by the ESR/ER will be different from that listed. Where possible the mobility allowance will be untaxed but this will depend on country tax regulations. Salaries converted into local currency are approximate at the time of writing and may change, each ESR/ER will receive their full Euro entitlement after taxes regardless of the host currency.

Our Researchers

Alfredo was born in Senigallia (Italy), he has lived in several Italian towns during his life. He studied physics at the University of Bari (Italy) and then particle physics during his Masters at the University of Pisa.

During his Masters thesis Alfredo built and studied a series of particle detectors prototypes: his results have been used to design the new drift chamber prototype for the MEG II experiment in Zurich (Switzerland).

Alfredo decided to move to engineering for a variety of reasons; the large collaborative groups in particle physics make it difficult to feel ownership of any new discovery and the idea of working on audio has always been something that had appealed, even before starting University. This project offered the right opportunity to merge his interests both in audio and the pure science fields. He is currently studying for his PhD on audio source separation at the University of Surrey (UK).

This individual project focusses on using Deep Neural Networks (DNNs) to predict the Direction Of Arrival (DOA) of audio sources in an audio mixture with respect to the listener. The DOA is then used to generate soft/binary time-frequency masks for the recovery/estimation of the individual audio sources.

Cian is from Tipperary, Ireland. He attended Trinity College Dublin where he obtained an Honours degree in Mathematics, during which time he also studied classical and jazz music at the Royal Irish Academy of Music. For his Masters degree, he spent two years at Georgia Institute of Technology and graduated with an MSc in Music Technology. His work focussed on sparse dictionary learning applications to Music Information Retrieval problems such as genre recognition and music mood estimation.

This individual project is Automatic Music Transcription (AMT). Given an audio file of a piece of music, the goal of AMT is to produce a "pitch-time" representation which gives the musical pitches present in the signal at each time-frame.

Wajiha is from Pakistan. She did her Masters at Abo Akademi University, Finland.
During her masters, she received a scholarship from Zeno Karl Schindler foundation, Geneva for her masters thesis in cardiovascular magnetic resonance (CVMR) research group in University Hospital Lausanne.
The aim of the thesis was to characterize the accuracy and precision of cardiac T2 mapping as a function of different influences such as signal-to-noise ratio, cardiac phases, off-resonance frequencies using both numerical simulations and in-vivo imaging.
The project was presented in International Society of Magnetic Resonance in Medicine meeting and received Magna Cum Laude award and subsequently was published as in magnetic resonance in medicine (MRM).

Also during her masters, Wajiha worked on another research project in the University of Oulu, Finland that involved studying imaging characteristics of articular cartilage degeneration with clinical ultrasound device which was presented in the Osteoarthritis Research Society meeting.
Wajiha's research interests are accelerated image acquisition and reconstruction in Magnetic Resonance Imaging and its clinical applications.

The general aim of the project is to utilize the compressed sensing theory and algorithm to accelerate Magnetic Resonance Imaging (MRI).
MRI is a useful clinical tool to diagnose diseases but is limited due to the lengthy acquisition time.
This is important as less amount of information is acquired due to slow acquisition that subsequently affects the image quality and also the patient have to spend substantial amount of time in the scanner.
The qualitative nature of the MR images can be addressed by quantifying the parameters (T1 and T2 relaxation) used to produce contrast in the image.
This is known as magnetic resonance parametric mapping. It is considered a valuable tool for the quantitative assessment of brain structure and function.
It has been proved to be useful in the study of brain ageing, Parkinson’s disease, epilepsy, multiple sclerosis etc.
However, the clinical utility of conventional parameter mapping is limited due to the lengthy acquisition times which subsequently results in low spatial resolution and employing low number of time points for parameter fitting.
The acquisition of undersampled data is a potential solution for faster parameter mapping which can be achieved by either parallel imaging\cite{MRM:MRM10044} or by compressed sensing (CS)\cite{lustig2007sparse}.
Compressed sensing has three major components; signal sparsity, incoherent sampling and non-linear sparsity-promoting reconstruction.
All three conditions are met naturally by MRI and thus making it an ideal application of CS.

Rodrigo was born in Brasilia, Brazil, in 1991. He did most of his undergraduate studies at the University of Brasilia (UnB), earning a degree in Electrical Engineering in 2014. In the meantime, he also studied Electronic Engineering for a year at ENSEIRB-MATMECA, in Bordeaux, France, in the context of a Brazil-France exchange program put in place by their governments. He started his graduate studies at UnB, focusing on image and video processing, more specifically on the incorporation of models for prediction of salient regions to automatic image and video quality estimation. Now a PhD candidate at EPFL, Rodrigo is working on the Signal Processing Laboratory 2 (LTS2), supervised by Prof. Pierre Vandergheynst.

Konstantinos is a PhD student at the Signal Processing Laboratory LTS2 at Ecole Polytechnique Federale de Lausanne.
His hometown is Thessaloniki located in the northern part of Greece.
It is there that he completed his Bachelor and Master's degrees in Electrical and Computer Engineering.
Konstantinos completed his masters project as an exchange student at EPFL, on the subject of dictionary learning for graph signals.
He is interested in supervised dictionary learning, graph signal processing and neural networks.

Milad is a PhD student in Instituto Superior Tecnico (IST), under supervision of Professor Mario Figueiredo.
He got his Bachelor and Masters degrees both from Iran.
His current research interests are statistical signal processing and image processing.

The topic of this research project is in patch-based image restoration, and is related to the work previously covered in Milad's MSc thesis, which has led to journal and conference publications.
The general goal of the current research is to improve and extend that previous in several directions.

Lina is from China and received a Bachelor of Science in geographic information system and a Bachelor of Economics from South China Normal University, Guangzhou, China, in 2012. After that, she got a M.S. degree at the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, in 2015. Currently, she is pursuing a Ph.D. degree in Instituto de Telecomunicacoes, Instituto Superior Tecnico, Lisbon, Portugal. Lina is working on inverse problems in hyperspectral imaging (namely, in unmixing, denoising, and superresolution), supervised by Prof. Jose M. Bioucas-Dias and Prof. Mario Figueiredo.

In hyperspectral imaging (HSI), the sensors measure the electromagnetic energy scattered in their instantaneous field view in hundreds (even thousands) of spectral channels. The very high spectral resolution of HSI enables a precise identification of the sensed materials via spectroscopic analysis, facilitating countless applications; e.g., earth observation and remote sensing, food safety, pharmaceutical process monitoring and quality control, as well as biomedical, industrial, and forensic applications.

My research focuses on inverse problems in hyperspectral imaging, namely unmixing, denoising, and superresolution.
Due to low spatial resolution of hyperspectral cameras, microscopic material mixing, and multiple scattering, the measurements acquired by those cameras are mixtures (linear and nonlinear) of spectra of materials in the scene under study. Thus, accurate estimation from acquired HSIs requires unmixing, which is a blind source separation (BSS) problem. Hyperspectral unmixing is a challenging ill-posed inverse problem, not only because it is a BSS, but also due to model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Among these degradation mechanisms, endmember variability and nonlinear unmixing are studied in my research under the Bayesian framework.

HSIs acquired by imaging instruments are often noisy owing to a number of degradation mechanisms such as electronic noise, Poissonian noise, quantization noise, and atmospheric effects. My research mainly considers the principal sources of noise, including Gaussian noise, Poissonian noise, and mixture of both noise. Owing to a very high spatial-spectral correlation and spatial self-similarity, this class of images has low rank structure and admit sparse representations on suitable frames. These characteristics of HSIs will be explored to devise fast and effective HIS denoising algorithms.

My research also involves the joint design of fast hyperspectral sensing strategies onboard and superresolution and fusion algoritms on ground stations. A computational imaging perspective is adopted in the joint design of “smart” sensing and computational algorithms to recover the original data. According to this perspective, the image is computed by algorithms designed cooperatively with the sensing strategy. The design of fast hyperspectral sensing strategies is of paramount importance in spaceborne hyprespectral systems due to the extremely large volumes of data collected by the imaging systems onboard satellites and the low bandwidth of the connections between them and the ground stations The fast sensing strategies will be conceived to yield easy, from a conditioning point of view, superresolution and fusion inverse problems. I’ll research acquisition strategies which adaptively control the blurriness and be able to acquire data with different resolutions for different spectral channels, having into consideration that hyperspectral images have low rank and a strong degree of selfsimilarity.

ESR8 Damien Scieur: Large-scale signal processing - INRIA, France

Damien started his studies in 2010 in Faculte Polytechnique de Mons (Belgium) in engineering, then he moved 2012 in École Polytechnique de Louvain (Belgium).
His masters was in applied mathematics with a particular focus on optimization.

He did his master thesis with Yurii Nesterov on "Global complexity analysis for the second-order methods".
The goal was to design efficient methods for some complex functional classes using both first and second order information.

Damien also worked inside the university with Raphael Jungers and Julien Hendrickx on switched systems.
The goal was to implement efficient algorithms to compute certificates on the stability of such systems in the context of a Matlab toolbox (\textit{JSR toolbox}, available online on the Mathworks website).

He also worked with Anthony Papavasiliou and Leopold Cambier on a Matlab toolbox which solve structured stochastic optimization problems (available on GitHub and on the website http://www.baemerick.be/fast/).
For example, this toolbox can be used to manage electricity production and consumption when some uncertain amount of energy is available (wind energy, solar panel, water dam, ...).

This project is working on acceleration algorithms. Like the name suggests, the goal of such an algorithm is to make other methods faster. The idea is to design a generic method which is able to accelerate the rate of convergence of some other methods without any knowledge of the methods.

The goal of this algorithm is non-trivial. If we can achieve this goal, this will lead to more efficient methods, allowing us to solve bigger problems in less time, or more accurately.

Zhongwei Xu received the B.S. in Electrical Engineering, and M.S. in computer science, from the Xidian University, China, in 2008, and 2011, respectively.
He received the Ph.D. in computer science from the Universitat Autonoma de Barcelona, Spain, in 2015. His research interests are the restoration and enhancement of digital images, and image and video coding.

The objective of this post-doc project is: multi-spectral 3D scene reconstruction using images from different bands.
It involves two main parts:
1) non-local 3D point cloud filtering, based on enhanced sparse representation in transform domain;
2) automatic co-registration for images from different spectral bands.